where \(f_{1}\)  and \(f_{2}\)  are the cut in and cut off frequencies, and \(sinc(x)=sin(x)/x\)  is the sinc function.
The cut in and cut off frequencies can be initialised according to any given frequency scale. Both \(f_1\)  and \(f_2\)  are learnable model parameters, though we consider both learnable and fixed configurations. Filter masking is applied to mask a number of the sinc filters. This is akin to channel drop-out [1]}, [2]} and frequency masking [3]}, [4]}, [5]} and acts to encourage the learning of better generalised representations. In practice, sinc filters in the range of \([C_{1}, C_{2})\)  are set to zero (masked), where \(C_{1}\)  is the first masked filter selected at random and \(C_{2}=C_{1}+f\)  is the last masked filter. The number of masked filters \(f\)  is chosen from a uniform distribution \([0, F]\) , where \(F\)  is a pre-defined maximum value. After \(f\)  is generated, \(C_{1}\)  is then chosen from a uniform distribution \([0, C-f]\) .
